Spectral imaging of deep tissue

ABSTRACT

Apparatus and methods are provided for the imaging of structures in deep tissue within biological specimens, using spectral imaging to provide highly sensitive detection. By acquiring data that provides a plurality of images of the sample with different spectral weightings, and subsequent spectral analysis, light emission from a target compound is separated from autofluorescence in the sample. With the autofluorescence reduced or eliminated, an improved measurement of the target compound is obtained.

BACKGROUND

Optical imaging of deep tissue is used to probe structures withinbiological specimens for laboratory research and biomedical purposes.This includes the imaging of internal organs and subdermal tissue inanimals such as mice, zebrafish, or humans, and one of the goals is tolearn about internal structures without surgery or other intrusivemeasures.

In one technique of deep tissue imaging, fluorescent probes which bindto a specific target in the specimen are imaged by exciting them withillumination light, causing them to fluoresce; the fluorescent emissionis separated from the illumination light, which has a differentwavelength, by barrier filters and then is detected using a verysensitive camera such as a cooled CCD detector. In other techniques, thespecimen is infected with agents that cause it to produce material thatis inherently fluorescent, with the most common example being greenfluorescent protein (GFP). Further techniques involve use of quantumdots as luminous probes.

As used herein, compounds such as fluorescent probes, GFP, or quantumdots, as well as related compounds or others used for similar purposes,are all termed the target compounds of a measurement.

The signals produced in such experiments are typically weak. In general,robust detection of the weak levels of light emitted from the deepstructures is beneficial because it provides earlier, or more reliable,detection of the structures being studied. Also, it may enable detectionof lower levels of the target compound. Accordingly, techniques orapparatus used for deep tissue imaging are valued if they offer a lowdetection threshold.

SUMMARY

The present invention features a method and apparatus to improvedetection of target compounds such as GFP, quantum dots, and fluorescentprobes used for deep tissue imaging in a variety of biological samples.Moreover, the method and apparatus are generally compatible with use ofsuch compounds, without requiring extreme sensitivity from the imagingdetector.

The method and apparatus use spectral imaging to distinguish betweenundesired autofluorescence signals arising from various sites in thespecimen, and the desired signal from the target compound. It has beendiscovered that the spectral information thus obtained provides improveddetection sensitivity and can be used for deep tissue imaging, eventhough there is typically significant optical loss in the spectralselection elements of the system.

In one embodiment, images are taken while viewing the emission lightfrom the specimen at a sequence of wavelengths, to develop an image cubewith two spatial dimensions and a spectrum at each point. By determiningthe difference in spectral properties between the desired targetcompound emission and the unwanted autofluorescence emission, theoverall signal is decomposed into components and the detection levelsfor the desired compound emission are greatly improved.

Other embodiments include measurements based on brightness ratios atseveral selected wavelengths or wavelength bands; measurements based onprincipal component analysis; and measurements based on neural networks,and on fuzzy logic.

In either case, the method and apparatus use spectral information todistinguish the desired signal emitted by the target compound from theunwanted autofluorescence signal, and thus to improve the measurementintegrity and sensitivity.

Various aspects and features of the invention will now be summarized.

In general, in one aspect, the invention features a method including:(i) illuminating a sample to cause the sample to emit radiation, whereinthe sample includes deep tissue supporting a target compound, andwherein the emitted radiation includes emission from the target compoundand emission from one or more other components in the sample; (ii)spectrally filtering the emitted radiation with each of a plurality ofdifferent spectral weighting functions; (iii) storing an image of thespectrally filtered radiation for each of the spectral weightingfunctions; and (iv) processing the stored images to construct a deeptissue image of the sample in which signal from the other compounds isreduced relative to signal from the target compound.

In general, in another aspect, the invention features a methodincluding: (i) providing a plurality of images of spectrally filteredradiation emitted from a sample in response to an illumination, whereinthe sample includes deep tissue supporting a target compound, whereinthe emitted radiation includes emission from the target compound andemission from one or more other components in the sample, and whereineach image corresponds to a different spectral weighting function; and(ii) processing the images of the spectrally filtered radiation toconstruct a deep tissue image of the sample in which signal from theother compounds is reduced relative to signal from the target compound.

In general, in yet another aspect, the invention features an apparatusincluding a computer readable medium which stores a program that causesa processor to: (i) receive a plurality of images of spectrally filteredradiation emitted from a sample in response to an illumination, whereinthe sample includes deep tissue supporting a target compound, whereinthe emitted radiation includes emission from the target compound andemission from one or more other components in the sample, and whereineach image corresponds to a different spectral weighting function; and(ii) process the images of the spectrally filtered radiation toconstruct a deep tissue image of the sample in which signal from theother compounds is reduced relative to signal from the target compound.

In general, in yet another aspect, the invention features an apparatuscomprising: (i) a sample holder configured to hold a sample includingdeep tissue, wherein the deep tissue supports a target compound; (ii) anillumination source configured to illuminate the sample to cause it toemit radiation, wherein the emitted radiation includes emission from thetarget compound and emission from one or more other components in thesample; (iii) an imaging system configured to image the emittedradiation to a detector; (iv) a tunable spectral filter configured tospectrally filter the emitted radiation with each of a plurality ofdifferent spectral weighting functions; (v) a detector configured tostore an image of the spectrally filtered radiation for each of thespectral weighting functions; and (vi) a electronic processor configuredto process the store images to construct a deep tissue image of thesample in which signal from the other compounds is reduced relative tosignal from the target compound. For example, the sample holder mayconfigured to hold an animal, such as a mammal, like a mouse, rabbit, orhuman. Also, for example, the imaging system may have a demagnificationgreater than or equal to 1, and, for example, the imaging system may beconfigured to image a field of view having a diagonal dimension greaterthan about 2 cm onto the detector.

Embodiments of the various aspects of the invention described above mayinclude any of the following features.

The sample including the deep tissue may be a living organism, such asan animal or a mammal. For example, the animal may include a mouse, arabbit, a zebrafish, or a human. Also, the deep tissue may be aninternal organ of the living organism, and

the deep tissue may lie within about 2 mm or more of the livingorganism.

The deep tissue may be subdermal tissue.

The emission from the other components of the sample may includeautofluorescence from tissue overlying the deep tissue.

The emission from the other components of the sample may includeautofluorescence from one or more layers of tissue in the sampledifferent from a layer of tissue including the deep tissue.

The target compound may be any of, for example, a fluorescent probebound to at least a portion of the deep tissue, a quantum dot bound toat least a portion of the deep tissue, a green fluorescent protein (GFP)bound to at least a portion of the deep tissue, a yellow fluorescentprotein (YFP) bound to at least a portion of the deep tissue, and a redfluorescent protein (RFP) bound to at least a portion of the deeptissue.

The emission from the target compound may be fluorescence.

At least some of the spectral weighting functions may correspond toparticular wavelength bands. For example, all of the spectral weightingfunctions correspond to particular wavelength bands. Alternatively, atleast some of the spectral weighting functions may correspond tosinusoidal weightings of multiple wavelength bands.

The spectral filtering may include using any of a liquid-crystal,tunable optical filter, an interferometric optical filter, and a filterwheel containing a plurality of band pass filters.

Each stored image may include an intensity value for each of multiplepixels.

Processing the stored images may include constructing the deep tissueimage based on a weighted superposition of signals in the stored images.

Processing the recorded images may include constructing the deep tissueimage based on the recorded images and at least one emission spectrumfor the other components in the sample. For example, constructing thedeep tissue image may include calculating a remainder spectrum for eachpixel in the set of stored images based on the at least one emissionspectrum for the other components.

Similarly, processing the recorded images may include constructing thedeep tissue image based on the recorded images and an emission spectrumfor the target compound. For example, constructing the deep tissue imagemay include calculating a remainder spectrum for each pixel in the setof stored images based on the emission spectrum for the target compound.

Also, processing the recorded images may include constructing the deeptissue image based on the recorded images, at least one emissionspectrum for the other components in the sample, and an emissionspectrum for the target compound. For example, constructing the deeptissue image may include solving at least one component of a matrixequation in which one matrix is based on the stored images, and anothermatrix is based on the emission spectra.

The deep tissue may support multiple target compounds and processing thestored images may include constructing a deep tissue image for each ofthe target compounds. For example, processing the recorded images mayinclude constructing the deep tissue images based on the recorded imagesand emission spectra for the target compounds. Furthermore, processingthe recorded images may include constructing the deep tissue imagesbased on the recorded images, the emission spectra for the targetcompounds, and at least one emission spectrum for the other componentsin the sample.

The plurality of the different spectral weighting functions may includefour or more spectral weighting functions.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. All publications, patentapplications, patents, and other references mentioned herein areincorporated by reference in their entirety. In case of conflict, thepresent specification, including definitions, will control.

Other features, objects, and advantages of the invention will beapparent from the following detailed description.

DESCRIPTION OF DRAWINGS

The invention will now be further described merely by way of examplewith reference to the accompanying drawings.

FIG. 1 is an image of a mouse which has been injected with a fluorescentprobe, imaged at a single spectral band of λ=530 nm.

FIG. 2 is a graph of the emission spectrum of autofluorescence and thatof the target compound for the sample of FIG. 1, with the light-coloredline showing the spectrum for the autofluorescence and the dark-coloredline showing the spectrum for the target compound.

FIG. 3 is an image of the target compound emissions from the mouse ofFIG. 1, with the autofluorescence signal removed using spectraltechniques in accordance with the present invention.

FIG. 4 is an image of the autofluorescence emissions from the mouse ofFIG. 1, with the target compound emissions separated using spectraltechniques in accordance with the present invention.

FIG. 5 is a flow-chart of one embodiment of the present invention, basedon acquisition of a spectral cube and subsequent analysis by linearunmixing.

FIG. 6 is graph of the spectral properties of each spectral band in theembodiment of FIG. 5.

FIG. 7 is a flow-chart of another embodiment which incorporates PCA toestimate the spectra of the autofluorescence and the target compound(s).

FIG. 8 is a flow-chart of yet another embodiment in which the spectralfiltering is performed interferometrically.

FIG. 9 is a graph of the spectral properties of the spectral weightingsused in the embodiment of FIG. 8.

FIG. 10 is a schematic diagram of a spectral imaging system.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

The invention features a method and apparatus for reducing the detectionlevel of a target compound in deep tissue through spectraldiscrimination. An important aspect is that a beneficial result isobtained despite the low light levels involved.

A schematic diagram of a spectral imaging system 100 for imaging deeptissue is shown in FIG. 10. System 100 includes a sample holder 110suitable for holding a specimen 112 having deep tissue. For example, thespecimen may be a living organism, such as an animal or mammal. A targetcompound is bound to selected portions of deep tissue in the specimen.An illuminator 120 (e.g., a metal halide lamp or other lamp, a laser, anlight emitting diode array, or any other source of electromagneticradiation) directs excitation light 122 to the specimen to exciteemission (e.g., fluorescence) from the target compound in the deeptissue. Typically, the excitation light will also cause theautofluoresence from the other components in the specimen. Therefore,the electromagnetic radiation 130 emitted from the specimen includesemission from the target compound as well as autofluorescence. Emittedradiation 130 is collected by imaging system 140 and imaged onto acamera 150.

Because system 100 is designed for imaging deep tissue in relativelylarge specimens (e.g., living organisms), the imaging system typicalprovides a demagnification of one or more, or even 2 or more. That is,the image on the camera is the same size or smaller than the objectfield of view for the imaging system. Also, the object field of view forthe imaging system is typically greater than about 2 cm (or greater thanabout 3 cm) along a diagonal dimension.

Furthermore, although FIG. 10 shows emitted radiation 130 as beingcollected from an opposite side of the specimen relative to excitationlight 122, in other embodiments, the emitted radiation can be collectedfrom the same side as, or at an angle to, that illuminated by theexcitation light. Moreover, illumination may be provided from multiplesides of the specimen.

Positioned between the specimen and camera 150 is a tunable opticalfilter 160 (e.g., a liquid crystal, tunable optical filter, aninterferometric optical filter, or a motorized filter wheel). Opticalfilter 160 spectrally filters emitted radiation 130 according to each ofa plurality of spectral weighting functions (for example, four or morespectral weighting functions). The spectral weighting functions maycorrespond to specific wavelength bands, or may be more complicatedfunctions such as a sinusoid distribution of pass bands. Camera 150records images of the spectral filtered emitted radiation 170 for eachof the different spectral weighting functions, and sends the image datato a computer 180 for analysis. As described in greater detail below,the computer processes the image data based on the different spectralweighting functions, and one or more emission spectra corresponding topure target compound, pure autofluorescence of one or more othercomponents of the specimen, or both, to construct a deep tissue imagethat suppresses the autofluorescence signal to reveal the targetcompound.

In what follows, we describe the context for the spectral imaging of thedeep tissue, a specific example of deep tissue imaging, and spectralunmixing techniques for constructing the deep tissue image.

It is a hallmark of imaging structures in deep-tissue samples via targetcompounds that the optical signals are relatively weak. Accordingly,many practitioners place prime importance on the properties of thedetector, and on the efficiency of all elements in the optical path,such as the lenses and the blocking filter used to block the excitationlight from reaching the detector. Yet while the present art of CCDdetectors and the like is suitable for detecting low light levelsignals, it does not adequately address the problem of discriminatingbetween light emitted by the target compound, and light from othersources such as autofluorescence. Thus one's detection level in practicemay be set by the level of confounding light arising from siteselsewhere within the specimen, rather than considerations such asreadout noise in one's detector, or the light gathering power of theobjective.

Put more precisely, one's detection limit can be seen as the greater ofone's detector noise or the confounding signal flux which is presentedto the detector; expressed in either case as the equivalentconcentration of target compound in the specimen to produce light ofthat signal level at the detector.

Unless the light emitted by the target compound dominates over all othersources in the specimen, one is often limited by the confounding signalrather than by one's detection apparatus. Some level of autofluorescenceis inherent in biological samples when they are illuminated with lightof visible range, especially when the light is green (550 nm) orshorter. So despite the use of optimized target compounds,autofluorescence arising at or near the specimen surface can often setthe detection limit.

Further, emission from target compounds within deep tissue can beattenuated by scattering or absorption as it travels from the site ofemission to the surface of the specimen. The signal level reaching theimaging system is thus reduced, while light that is generated at thesurface layer of the specimen is not similarly attenuated. The detailsof this effect depend on the geometry of the sample specimen relative tothe collection optics, as well as the optical properties of the sample.

Likewise, the illumination light may be attenuated or scattered as ittravels from the source of illumination through the surface layers ofthe specimen on its way to the structure being imaged. The excitationsignal reaching the target site is reduced, while the signal developedat the surface of the specimen is not similarly attenuated. The detailsof this depend on the geometry of the illumination and collectionoptics, as well as on the optical properties of the sample specimen.

These considerations can exacerbate the effect of autofluorescence byincreasing the relative contribution of autofluorescence emission to thedetected signal, compared with emission from the target compound.

The magnitude of the problem is illustrated by FIG. 1, which is an imageof the fluorescent emission from a mouse. The mouse was illuminated withlight of approximately 480 nm, and the emission light was filtered by a25 nm bandpass filter centered at 530 nm. There is a tumor in the lungof the mouse which expresses the green fluorescent protein (GFP). Yetthe tumor is not easily distinguishable in the image due to an equal orgreater signal from generalized autofluorescence, apparently developedin the dermal layers of the mouse. As a result, while the signal levelat any point in the image is easily quantified, the presence of targetcompound, and thus the tumor, cannot be confirmed due to high levels ofbackground autofluorescence which sets an equal or higher detectionthreshold than the target compound involved.

Autofluorescence is also variable from specimen to specimen and can beunpredictable. Thus if an absolute flux level is used to makeassessments about the target compound, one can obtain false positivereadings. Variability can arise from factors such as mold or disease onthe skin of the specimen. These are typically not uniform across thespecimen. So if one seeks to detect the presence of a target compound bycomparing local signal levels in a given region against the mean levelfor the specimen, results are also not reliable.

It is possible in some cases to reduce autofluorescence by choice of theillumination wavelength. Generally the use of longer wavelengths forillumination is beneficial, as is known in the art, since they typicallygenerate less autofluorescence. Also, it can be beneficial to choose atarget compound whose emission light occurs at a different wavelengthrange from the autofluorescence of the specimen. Yet it is not possibleto choose wavelengths that is free of crosstalk, as shown by FIG. 2. Theemission spectra of the target compound, GFP (shown by the dark-coloredline), and of the autofluorescence (shown by the light-colored line),are completely overlapping. At any wavelength where the target hassubstantial emission, the autofluorescence is also strong, soautofluorescence cannot be eliminated by use of a fixed optical filteror something similar. Nor does a color camera discriminate between twosuch similar green spectra.

Yet as FIG. 2 indicates, the spectra of GFP and autofluorescenceemissions are nonetheless different. Thus, in accordance with theinvention, a spectral imaging approach can distinguish the two andeliminate or greatly reduce the contribution of the latter signal.

In a first embodiment of the invention, this is achieved withconventional apparatus to illuminate the specimen and to block theillumination light from entering the detector. This can be done using anilluminator such as the LT-9500 MSYS from Lighttools Research(Encinitas, Calif.) together with a longpass optical filter thattransmits substantially all light λ>510 nm, placed in the path of theobjective.

The spectral imaging detector consists of a QImaging 1300C digitalcooled CCD camera (Roper Scientific, Trenton N.J.) with a 55 mm F/2.8Nikkor macro lens (Nikon USA, Melville N.Y.), to which a VARISPECtunable optical filter (CRI Inc, Woburn Mass.) is coupled with amounting adaptor. The VARISPEC filter is a computer-controlled opticalfilter with 25 nm bandpass and tunable center wavelength. These areconnected to an IBM Thinkpad computer which controls the imageacquisition and performs the data analysis. Communication is via anIEEE-1394 interface to the camera, and an RS-232 interface to theVARISPEC filter.

The VARISPEC filter uses nematic liquid crystal variable retarderelements to constuct a tunable Lyot filter. The variable retarders areplaced in optical series with fixed waveplates of quartz or othermaterial, to produce a retardance that is well-known and electricallyadjustable. Linear polarizers between successive stages of the filteract to block unwanted orders so only a single peak is transmitted, andout-of-band leakage can be reduced to 0.01% if desired. By choice of theretarder thicknesses, one may obtain bandwidths ranging from 0.1 nm to50 nm or more. Tuning action is rapid (<50 ms) and there is no imageshift from tuning, which is valuable for imaging applications.

The mouse was imaged by taking a sequence of images while the centerwavelengh of the VARISPEC filter was tuned from 500 nm to 650 nm. Theresult is an image cube, with a full two-dimensional image of thesample, and a full spectrum at every point in the image. The exactspectrum recorded at a given point depends on the amount of GFP andautofluorescence, and on the two spectra, as:S(x,y,λ)=a(x,y)*F(λ)+b(x,y)*G(λ)  [1],where (x, y) indices are used to denote a given pixel location in theimage, the asterick “*” denotes multiplication, λ is used to denote agiven wavelength of emission or detection, and

S(x, y, kλ) denotes the net signal for a given location and wavelength,

F(λ) denotes the emission spectrum of autofluorescence,

G(λ) denotes the emission spectrum of GFP,

a(x, y) indicates the abundance of autofluorescence at a given (x, y)location, and

b(x, y) indicates the abundance of GFP at a given (x, y) location.

Equation [1] states that the net signal from a given location is the sumof two contributions, weighted by the relative amount ofautofluorescence and GFP present. It is easier to see if one writes theabove equation for a single pixel:S(λ)=aF(λ)+bG(λ)  [2].F and G may be termed the spectral eigenstates for the system, which arecombined in various amounts according to the amount of autofluorescenceand GFP emission, to produce an observed spectrum S.

Now if the emission spectra of the autofluorescence and of the GFP areknown (or can be deduced, as described below), one may invert equation[2] by linear algebra to solve for a and b, provided that the spectrum Shas at least two elements in it; i.e. that one has data for at least twoemission wavelengths λ. Then we can writeA=E⁻¹S  [3],where

A is a column vector with components a and b, and

E is the matrix whose columns are the spectral eigenstates, namely [FG].

Using equation [3] one can take the observed spectrum and calculate theabundance of the autofluorescence and of the GFP sources. This processmay be repeated for each pixel in the image, to produce an image of GFPthat is free of contributions from autofluorescence. As a result, thedetection level is greatly enhanced.

Note that the matrix E need only be inverted once for a given set ofautofluorescence and target compound spectra, so the calculation ofabundances is not burdensome and can be readily done in nearly real-timeby a personal computer.

The results of this process are shown in FIGS. 3 and 4, which presentthe abundance images for GFP and autofluorescence, respectively. As FIG.3 shows, it is easy to detect the GFP once it is separated from theautofluorescence. The degree of improvement in the GFP image isstriking. Also, one can see that the autofluorescence image is smoothand unaffected in the region where GFP is present, which is consistentwith the fact that the presence of GFP in a deep tissue structure shouldnot alter the amount of autofluorescence emission from the overlyingdermal regions.

Overall, the measurement and analysis process is shown as a blockdiagram in FIG. 5. The specimen is prepared and illuminated (step 505)and the spectral bands to be acquired determined (step 510). Then thespectral filter is set to transmit the spectral weighting functionI_(i), for example, a particular wavelength band (step 515), and animage corresponding to that spectral weighting function is acquired(step 520). The spectral filter is then set to the next spectralweighting function and the corresponding image acquired until all bandshave been acquired (steps 525 and 530). The spectra for the targetcompound and the autofluorescence is then provided or otherwisedetermined (step 535). Based on the spectra, the matrix E is generatedand its inverse determined (step 540). For each image pixel, the spectradefined by the series of acquired images is then multiplied by theinverse matrix of E (step 545) to generate an abundance image of thetarget compound(s), i.e., the deep tissue image (step 550).

In this example, the invention permitted observation of structures intissue lying ˜2 mm within a living organism, where the overlying dermisis at least 300 microns thick and has significant autofluorescence. Theinvention has also been used to image structures at differing depths inother specimens, including non-mammalian specimens such as zebrafish. Inthe latter, the specimen is physically thinner, but once again there isthe problem of autofluorescence arising from other layers in thespecimen, which confounds the detection of target compounds in theinterior of the specimen. While there are optical techniques for depthsectioning, such as confocal microscopy, the present invention providesa simple and practical alternative.

Nothing about this invention prevents one from viewing multiple targetcompounds per specimen. If we denote the number of spectral settings asn, matrix E becomes an n×m matrix instead of an n×2 matrix used in theabove example. So, one can use the invention to remove autofluorescencefrom a sample which contains two target compounds; or to remove twotypes of autofluorescence from a sample with one or more targetcompounds. In any case, the result is the isolation of the targetcompound(s) from the autofluorescence, and the ability to quantify oneor all of these components.

The limit to the number of compounds that can be isolated, and to thesignal to noise generally, is given by the shot noise levels and thedegree of spectral distinction between the emission spectra of thespecies being distinguished (including autofluorescence). One candescribe the degree of correlation between two spectra by an angle θ,defined byθ=arc cos [(S ₁ ·S ₂)/(|S ₁ ||S ₂|)]  [4].

Sets of spectra for which θ is small for two members are not as easilyseparated into their components. Physically, the reason for this iseasily understood: if two spectra are only marginally different, it isharder to determine which species was present, and noise can easilychange one's estimate of relative abundances. Criteria such as θ can beused to help decide what spectral bands are appropriate for ameasurement, and one may try and select bands that yield a large θwhenever possible. Or, one may make an empirical study of what bandsyield the best separation, by trial and error. It can be helpful toinclude more bands than would appear necessary from mathematicalanalysis alone, in order to reduce sensitivity to slight spectral shiftsfrom the expected shapes, as may occur due to variation betweenspecimens and the like.

It is worth considering the optical efficiency of the measurementapparatus in the above embodiment, to understand where the inventiveimprovement comes from. First, the lens used was an F/2.8 type insteadof an F/1.2 or F/1.8 which is more typical for this work, and thischoice results in 2.4-5.4× less light collection. Next, the VARISPECfilter has a transmission of approximately 25 percent, and collects overa 25 nm range, in contrast to a typical interference filter which has atransmission of 80 percent and collects over a 40 nm range. This furtherreduces the sensitivity by a factor of 5.1× compared to equipment thatmight be used for this work, for an overall reduction in light flux of12.3×-27.8× compared to the best practice alternatives of the art.

The CCD camera is cooled 25° below ambient to approximately 0° C., whichis typical for an ordinary CCD sensor, unlike the sensors used inimaging stations such as the ChemiPro system from Roper Scientific(Trenton, N.J.)., which is cooled with liquid nitrogen to attaintemperatures 100° below ambient or lower.

As this suggests, the effectiveness of this technique does not arisefrom extreme efficiency in the gathering or collection of light; ratherit comes from using spectral selectivity to identify and remove theeffects of background fluorescence.

Turning now to the question of how the spectra F and G are determined,any method may be used which yields an adequate estimate of the spectrainvolved. For some target compounds, there is a known spectrum for thematerial from published references. Alternatively, with a spectralimaging station as is used in the invention, one may obtain the spectrumdirectly by placing a sample containing a sufficient concentration ofthe target compound in front of the imager and taking its spectrum.Conversely, it is often possible to image a region of the specimen whereone has a priori knowledge that there is no target compound in thatregion, and in this way one can obtain an estimate of that component.

Various data analysis techniques can be used in this process, such asprincipal component analysis (PCA), which identifies the most orthogonalspectral eigenvectors from an image cube, and yields score imagesshowing the weighting of each eigenvector throughout the image. If PCAanalysis is performed on an image that contains contributions from thetarget compound(s) and from the background autofluorescence, the vectorsfrom PCA can be used to develop estimates of the spectra involved.

This may be done in combination with other mathematical processing, andthere are other known techniques for identifying low-dimensionalityspectral vectors, such as projection pursuit, a technique described inL. Jimenez and D. Landgrebe, “Hyperspectral Data Analysis and FeatureReduction Via Projection Pursuit”, IEEE Transactions on Geoscience andRemote Sensing. Vol. 37, No. 6, pp. 2653-2667, November 1999.

An embodiment that incorporates PCA is shown in FIG. 7 in block-diagramform. The block diagram is similar to that of FIG. 5, except that step535 is replaced with performing a PCA analysis (step 735) anddetermining eigenvectors corresponding to the autofluorescence andtarget compound(s) (step 737) for use in the matrix E in step 540.

Whatever method is used in the determination, the goal is to derivespectra corresponding to the species sought, whether the determinationis made by a direct measurement of relatively pure samples of the targetcompound and the autofluorescence, or a mathematical analysis of spectrathat may contain mixtures of both components, so long as adequatespectral estimates are obtained.

While the embodiments discussed above use spectral estimates for boththe autofluorescence emissions and target compound emissions, this isnot essential. In some cases, one may seek to reduce theautofluorescence without having a priori knowledge of the targetcompound spectrum. This can be done by looking at the signal in a givenpixel, and subtracting from it the maximum amount of autofluorescencewhile leaving the remaining signal that is positive definite in allspectral channels. That is, one defines a so-called remainder spectrumR_(a)(λ) for each pixel:R _(a)(λ)=S(λ)−aF(λ)  [5a],and then selects the largest value of parameter a consistent withR_(a)(λ) having a non-negative value in every spectral channel. Theresulting spectrum R_(a)(λ) is then used as the sample spectrum,expunged of autofluorescence. One may also make the determination of abased not on strict non-negative criterion listed above, but on somerelated criteria that incorporates a small negative distribution, toaccount for considerations such as shot noise or detector noise.

Alternatively, one may seek to determine the distribution of the targetcompound by a similar method when its emission spectrum is known, butthe autofluorescence spectrum is not, by seeking to subtract off fromS(λ) the maximum amount of target emission G(λ) consistent with apositive remainder, and then reporting the amount that was subtracted ateach pixel as the image of the target compound. In this case, theremainder spectrum R_(b)(λ) for each pixel is given by:R _(b)(λ)=S(λ)−bG(λ)  [5b],where one selects the largest value of parameter b consistent withR_(b)(λ) having a non-negative value in every spectral channel.

Furthermore, the technique described above in connection with Equations5a and 5b can be expanded to cases where the spectra for one or moreadditional components of the sample are known, and one wants to removetheir contribution to the signal. In such cases the remainder spectrumis rewritten to subtract a contribution of each such component from theobserved signal based on the additional spectra and consistent with apositive remainder in each spectral channel.

Another preferred embodiment uses the same apparatus to illuminate andview the specimen, except that the specimen is a zebrafish in an aqueoussample stage.

Yet another alternative uses the same apparatus to view a mouse that hasbeen transfected to express either the yellow fluorescent protein (YFP)or the red fluorescent protein (RFP), or both, and produces images ofthe target compound(s) after removal of the autofluorescence signal.There are also mutant strains developed which may also be used. Any ofthese may be combined with the GFP when that produces useful results.

Further alternative embodiments view mice containing target compoundsbased on quantum dots, and incorporate an illuminator and filter setoptimized for the quantum dot species involved.

An embodiment operating in the infrared range 600-1100 nm may also beconstructed using a near-infrared VARISPEC filter such as the modelVIS-NIR2-10-20HC.

In an alternative embodiment, the VariSpec filter is replaced with amotorized filter wheel containing a plurality of bandpass filters. Yetanother embodiment uses a split-image system from Optical Insights(Tucson, Ariz.) to view the specimen in four spectral bands at once,albeit with lower spatial resolution. The bands are chosen to give aspectrum that distinguishes between the target compound and backgroundautofluorescence, i.e. to have cos θ that is significantly differentfrom 1, preferably 0.8 or less.

It is not necessary that the images used for the spectral analysis beacquired with bandpass filters, only that the spectral weightings of thevarious images be different. For example, one could use aninterferometer to acquire the spectral information, as shown in FIG. 8in block-diagram form. The spectral response of the interferometer isshown in FIG. 9 for some selected values of path difference Z. Imagesthus obtained can be used for practicing the invention, either directlyor after transforming from interferograms to spectra. The suitability ofusing the interferograms can be checked by looking at how well theydistinguish between the species involved, which can be determined bymeasures such as cos θ or by experimental study.

The block diagram of FIG. 8 is similar to that of FIG. 5 except that:steps 515, 520, 525, and 530 are replaced with corresponding steps 815,820, 825, and 830, which use a interferogram as the spectral weightingfunction rather than particular spectral bands; there is an optionalstep 832, which describe Fourier transforming the spectrally filteredimages to generate a spectral cube; and step 535 is replaced with step835 determines spectra or interferogram weightings for the targetcompound and the autofluorescence consistent with the form of theacquired data (and optional Fourier transform) for use in generating thematrix E.

The interferometer can be a mechanical type such as a Sagnac design, orit can be a birefringent interferometer as described in U.S. Pat. No.6,421,131, “Birefringent interferometer”. The latter uses fixed retarderelements such as quartz plates, together with switching apparatus, tomake the retarders add or cancel one another, so that using theseelements, along with variable retarder elements, one can produce anydesired retardance within a wide range. When polarized light encountersthis assembly, its polarization state is changed in a manner thatdepends on the wavelength of light, and this can be detected at an exitanalyzer polarizer. The spectral response at any particular setting ofthe interferometer is a sinusoid in 1/λ, after allowing for dispersion.By taking a sequence of readings at known retardance values, andperforming a fourier transform, the spectrum of the light can bedetermined. Such apparatus can be used in imaging systems to obtain aspectrum at every point in an image, or simply to obtain a set of imageswith various sinusoidal spectral response functions in the members ofthe set.

Indeed, any spectral imaging apparatus can be used provided that ityields adequate spectral information to distinguish emission by thetarget compound from background autofluorescence.

The spectral analysis and construction of the deep tissue image can beimplemented in hardware or software, or a combination of both. Theelectronic processing can be implemented in computer programs usingstandard programming techniques following the methods described herein.Program code is applied to input data to perform the spectral unmixingfunctions described herein and generate output information such as thedeep tissue image. The output information is applied to one or moreoutput devices such as a display monitor.

Each program is preferably implemented in a high level procedural orobject oriented programming language to communicate with a computersystem. However, the programs can be implemented in assembly or machinelanguage, if desired. In any case, the language can be a compiled orinterpreted language. Moreover, the program can run on dedicatedintegrated circuits preprogrammed for that purpose.

Each such computer program is preferably stored on a storage medium ordevice (e.g., CD-ROM or magnetic diskette) readable by a general orspecial purpose programmable computer, for configuring and operating thecomputer when the storage media or device is read by the computer toperform the procedures described herein. The computer program can alsoreside in cache or main memory during program execution. For example,computer 180 in FIG. 10 may includes a processor, an input/outputcontrol card, a user interface, such as a keyboard and monitor, and amemory. A program stored on a computer-readable medium is stored in thecomputer memory, and when executed, the program causes the processor tocarry out the steps of analyzing the spectrally filtered images.

While certain embodiments have been described, alternatives may be usedthat achieve the same end without deviating from the spirit of theinvention, according to the requirements of the task at hand and normaldesign considerations. It is explicitly intended that this invention canbe combined with the arts of instrument design, multispectral dataanalysis and image analysis, and optical system design, as appropriatefor a given use.

Additional aspects, features, and advantages are within the scope of thefollowing claims.

1. A method comprising: illuminating a sample to cause the sample toemit radiation, wherein the sample is a living mammal comprising deeptissue supporting a target compound, wherein the emitted radiationcomprises emission from the target compound and emission from one ormore other components in the sample, wherein the emission from the othercomponents of the sample comprises autofluorescence from one or morelayers of tissue in the sample different from a layer of tissuecomprising the deep tissue, and wherein the deep tissue is subdermaltissue; spectrally filtering the emitted radiation with each of at leastfour different spectral weighting functions; storing an image of thespectrally filtered radiation for each of the spectral weightingfunctions; and processing the stored images to construct a deep tissueimage of the sample in which signal from the other compounds is reducedrelative to signal from the target compound, wherein processing thestored images comprises constructing the deep tissue image based on aweighted superposition of information in the stored images and at leastone emission spectrum for the other components in the sample.
 2. Themethod of claim 1, wherein the animal comprises a mouse or a human. 3.The method of claim 1, wherein the deep tissue is an internal organ ofthe living mammal.
 4. The method of claim 1, wherein the deep tissuelies within about 2 mm or more of the living mammal.
 5. The method ofclaim 1, wherein the emission from the other components of the samplecomprises autofluorescence from tissue overlying the deep tissue.
 6. Themethod of claim 1, wherein the target compound is a fluorescent probebound to at least a portion of the deep tissue.
 7. The method of claim1, wherein the target compound is a quantum dot bound to at least aportion of the deep tissue.
 8. The method of claim 1, wherein the targetcompound is a green fluorescent protein (GFP) bound to at least aportion of the deep tissue.
 9. The method of claim 1, wherein the targetcompound is a yellow fluorescent protein (YFP) bound to at least aportion of the deep tissue.
 10. The method of claim 1, wherein thetarget compound is a red fluorescent protein (RFP) bound to at least aportion of the deep tissue.
 11. The method of claim 1, wherein theemission from the target compound is fluorescence.
 12. The method ofclaim 1, wherein at least some of the spectral weighting functionscorrespond to particular wavelength bands.
 13. The method of claim 12,wherein all of the spectral weighting functions correspond to particularwavelength bands.
 14. The method of claim 1, wherein at least some ofthe spectral weighting functions correspond to sinusoidal weightings ofmultiple wavelength bands.
 15. The method of claim 1, wherein thespectral filtering comprises using a liquid-crystal, tunable opticalfilter.
 16. The method of claim 1, wherein the spectral filteringcomprises using an interferometric optical filter.
 17. The method ofclaim 1, wherein the spectral filtering comprises using a filter wheelcontaining a plurality of band pass filters.
 18. The method of claim 1,wherein each stored image comprises an intensity value for each ofmultiple pixels.
 19. The method of claim 1, wherein constructing thedeep tissue image comprises calculating a remainder spectrum for one ormore pixels in the set of stored images.
 20. The method of claim 1,wherein processing the stored images comprises constructing the deeptissue image based on the stored images, the at least one emissionspectrum for the other components in the sample, and an emissionspectrum for the target compound.
 21. The method of claim 20, whereinconstructing the deep tissue image comprises solving at least onecomponent of a matrix equation in which one matrix is based on thestored images, and another matrix is based on the emission spectra. 22.The method of claim 1, wherein the deep tissue supports multiple targetcompounds and processing the stored images comprises constructing a deeptissue image for each of the target compounds.
 23. The method of claim22, wherein processing the stored images comprises constructing the deeptissue images based on the stored images and emission spectra for thetarget compounds.
 24. The method of claim 23, wherein processing thestored images comprises constructing the deep tissue images based on thestored images, the emission spectra for the target compounds, and atleast one emission spectrum for the other components in the sample. 25.The method of claim 1, wherein the emission comprises emission from athickness of tissue at least 2 mm thick.
 26. A method comprising:providing at least four images of spectrally filtered radiation emittedfrom a sample that is a living mammal in response to an illumination,wherein the sample comprises deep tissue supporting a target compound,wherein the emitted radiation comprises emission from the targetcompound and emission from one or more other components in the sample,wherein the emission from the other components of the sample comprisesautofluorescence from one or more layers of tissue in the sampledifferent from a layer of tissue comprising the deep tissue, wherein thedeep tissue is subdermal tissue, and wherein each image corresponds to adifferent spectral weighting function; and processing the images of thespectrally filtered radiation to construct a deep tissue image of thesample in which signal from the other compounds is reduced relative tosignal from the target compound, and wherein processing the imagescomprises constructing the deep tissue image based on a weightedsuperposition of information in the stored images and at least oneemission spectrum for the other components in the sample.
 27. The methodof claim 26, wherein the emission from the other components of thesample comprises autofluorescence from tissue overlying the deep tissue.28. The method of claim 26, wherein the emission comprises emission froma thickness of tissue at least 2 mm thick.
 29. The method of claim 26,wherein at least some of the spectral weighting functions correspond toparticular wavelength bands.
 30. The method of claim 29, wherein all ofthe spectral weighting functions correspond to particular wavelengthbands.
 31. The method of claim 26, wherein at least some of the spectralweighting functions correspond to sinusoidal weightings of multiplewavelength bands.
 32. The method of claim 26, wherein constructing thedeep tissue image comprises calculating a remainder spectrum for one ormore pixels in the set of stored images.
 33. The method of claim 26,wherein processing the stored images comprises constructing the deeptissue image based on the stored images, the at least one emissionspectrum for the other components in the sample, and an emissionspectrum for the target compound.
 34. The method of claim 33, whereinconstructing the deep tissue image comprises solving at least onecomponent of a matrix equation in which one matrix is based on thestored images, and another matrix is based on the emission spectra.